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8th International Conference on Computational Intelligence and Applications, ICCIA 2023

作者： Marisno, Denny Chin, Cheng Siong Shan, Chunlai See, Simon Newcastle University in Singapore Faculty of Science Agriculture Engineering Singapore Northwest Institute of Mechanical and Electrical Engineering Simulation and Test Technology Department Xianyang China Shanghai Jiao Tong University 800 Dongchuan RD. Minhang District Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9798350339741

Estimating the State of Charge (SoC) is a crucial part of a battery management system (BMS) for the electric ship. An Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the SoC using the ANFIS under different ambient temperature variations is proposed. A five-layer Artificial Neural Network for SoC estimation is used. The ANFIS achieves the lowest prediction error compared to the Neural Network, electrical circuit model, coulomb counting, Kalman Filter, Fuzzy Neural Network, and Support Vector Machine method. © 2023 IEEE.

关键词： Battery management systems

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Linking network- and neuron-level correlations by renormalized field theory

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Physical Review Research 2024年第3期6卷 033264页

作者： Michael Dick Alexander van Meegen Moritz Helias Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10) Department of Computer Science 3 - Software Engineering Peter Grünberg Institut (PGI-1) and Institute for Advanced Simulation (IAS-1) Institute of Zoology Department of Physics Faculty 1

It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analog of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.

关键词： Neuronal network activity Statistical field theory Cortical networks Critical phenomena Dynamical mean field theory

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Predicting the Geometric Location of Critical Edges in Adaptive GDSW Overlapping Domain Decomposition Methods Using Deep Learning 26th

Predicting the Geometric Location of Critical Edges in Adapt...

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26th International Conference on Domain Decomposition Methods, 2020

作者： Heinlein, Alexander Klawonn, Axel Lanser, Martin Weber, Janine Faculty of Electrical Engineering Mathematics & Computer Science Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 CD Delft72628 Netherlands Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Köln50931 Germany Center for Data and Simulation Science University of Cologne Köln Germany

ISBN: (纸本)9783030950248

For complex model problems with coefficient or material distributions with large jumps along or across the domain decomposition interface, the convergence rate of classic domain decomposition methods for scalar elliptic problems usually deteriorates. In particular, the classic condition number bounds [1, 12] will depend on the contrast of the coefficient function. As a remedy, different adaptive coarse spaces, e.g. [4, 13], have been developed which are obtained by solving certain generalized eigenvalue problems on local parts of the interface, i.e., edges and/or faces. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Domain decomposition methods

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Lindblad dynamics from spatio-temporal correlation functions in nonintegrable spin-1/2 chains with different boundary conditions

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Physical Review Research 2024年第2期6卷 023251-023251页

作者： Markus Kraft Jonas Richter Fengping Jin Sourav Nandy Jacek Herbrych Kristel Michielsen Hans De Raedt Jochen Gemmer Robin Steinigeweg Department of Mathematics/Computer Science/Physics Department of Physics Institut für Theoretische Physik Institute for Advanced Simulation Jülich Supercomputing Centre 1000 Ljubljana Slovenia Institute of Theoretical Physics Faculty of Fundamental Problems of Technology Zernike Institute for Advanced Materials

We investigate the Lindblad equation in the context of boundary-driven magnetization transport in spin-1/2 chains. Our central question is whether the nonequilibrium steady state of the open system, including its buildup in time, can be described on the basis of the dynamics in the closed system. To this end, we rely on a previous study [Heitmann et al., Phys. Rev. B 108, L201119 (2023)], in which a description in terms of spatio-temporal correlation functions was suggested in the case of weak driving and small system-bath coupling. Because this work focused on integrable systems and periodic boundary conditions, we here extend the analysis in three directions: (1) We consider nonintegrable systems, (2) we take into account open boundary conditions and other bath-coupling geometries, and (3) we provide a comparison to time-evolving block decimation. While we find that nonintegrability plays a minor role, the choice of the specific boundary conditions can be crucial due to potentially nondecaying edge modes. Our large-scale numerical simulations suggest that a description based on closed-system correlation functions is a useful alternative to already existing state-of-the-art approaches.

关键词： Nonequilibrium statistical mechanics Open quantum systems Quantum transport 1-dimensional spin chains Quantum many-body systems Heisenberg model Lindblad equation Master equation Monte Carlo methods Quantum master equation Spin chains

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A Machine-Learning-based Surrogate Modeling Methodology for Submodel Integration in the Holistic Railway Digital Twin Platform 33

A Machine-Learning-based Surrogate Modeling Methodology for ...

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33rd CIRP Design Conference

作者： Zhou, Shiyang Meierhofer, Alexander Kugu, Ozan Xia, Yuxi Grafinger, Manfred TU Wien Institute of Engineering Design and Product Development Lehargasse 6/307 Vienna1060 Austria Virtual Vehicle Research GmbH Co-Simulation and Software Group Inffeldgasse 21a Graz8010 Austria University of Vienna Faculty of Computer Science Kolingasse 14-16 Vienna1090 Austria

A holistic railway infrastructure digital twin (DT) platform is sophisticated and consists of a series of submodels (e.g., turnouts, tracks, vehicles, etc.) that are built through various methodologies and software. However, integrating these submodels into the DT platform is tremendously challenging due to considerable computational complexity, software and interface restrictions. To this end, we designed a machine learning (ML) based surrogate modeling methodology for the submodel integration in the holistic railway infrastructure DT platform and illustrated the methodology through a case study. In this case study, an ML-based surrogate model for multibody simulation of railway vehicle-track dynamics is created, which can replace the railway vehicle-track simulation executed with the Multibody Dynamics (MBD) simulation commercial software SimPACK. The well-built ML model can accurately and quickly predict the vehicle-track system's dynamic responses to different track irregularities. Besides, the integration process of the ML-based surrogate model into the DT platform through a standardized open-source Functional Mock-up Interface (FMI) is also proposed. The developed surrogate modeling methodology shows great promise owing to its high fidelity, which is verified by the measurement data collected from the Austrian national railway track system. The main contribution of our work lies in the well-built ML-based surrogate modeling methodology for reducing the computation complexity and time of different submodels, which facilitates the unification and integration of different submodels. Furthermore, this approach can also be applied to other submodels and help to build the holistic railway DT platform collaboratively. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Machine learning

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Multilabel segmentation and analysis of skeletal muscle and adipose tissue in routine abdominal CT scans

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computers in Biology and Medicine 2025年 186卷 109622-109622页

作者： Kreher, Robert Hille, Georg Preim, Bernhard Hinnerichs, Mattes Borggrefe, Jan Surov, Alexey Saalfeld, Sylvia Department of Simulation and Graphics Faculty of Computer Science University of Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Radiology and Nuclear Medicine University Hospital Magdeburg Germany Department of Radiology Neuroradiology and Nuclear Medicine Johannes Wesling University Hospital Minden Germany Department of Computational Medicine Ilmenau University of Technology Germany

Purpose: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra and additionally improves the segmentation of the intermuscular fat. Method: Based on a self-created data set of 130 patients, an extended Unet structure was trained and evaluated with the help of Dice score, IoU and Pixel Accuracy. In addition, the interobserver variability for the decision between ground truth and post-processed segmentation was calculated to illustrate the relevance in everyday clinical practice. Results: On average, the presented approach achieved 91.0±0.065% DSC for the muscle tissues and 88.9±0.062% DSC for the adipose tissues. It was shown that by post-processing the intermuscular fat tissue, physicians prefer the result of the algorithm presented in the paper to their segmentation by 91.51%. Conclusion: The algorithm provided more precise segmentations of muscles and adipose tissue, demonstrating high-quality performance in segmenting muscle tissue. In qualitative evaluations, physicians preferred the algorithm's segmentation over expert segmentations, with the preference quantified as 91.51%. This indicates that, based on their assessment, the algorithm's results were significantly favored. This qualitative feedback supports the algorithm's use in subsequent analyses of patient fitness, leveraging the detailed information it provides. © 2025 The Authors

关键词： Muscle

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Facial Feature Removal for Anonymization of Neurological Image Data

Facial Feature Removal for Anonymization of Neurological Ima...

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作者： Gießler, Fina Reithmann, Max Preim, Bernhard Behme, Daniel Saalfeld, Sylvia Department for Simulation and Graphics Otto-von-Guericke University Magdeburg Germany Department of Neuroradiology University Hospital of Magdeburg Germany Faculty of Computer Science and Research Campus STIMULATE Otto-von-Guericke University Magdeburg Germany

Interdisciplinary exchange of medical datasets between clinicians and engineers is essential for clinical research. Due to the Data Protection Act, which preserves the rights of patients, full anonymization is necessary before any exchange can take place. Due to the continuous improvement of image quality of tomographic datasets, anonymization of patient-specific information is not sufficient. In this work, we present a prototype that allows to reliably obscure the facial features of patient data, thus enabling anonymization of neurological datasets in image space. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Clinical research

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Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-Based Distributed Systems

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computers, Materials & Continua 2021年第2期66卷 1577-1594页

作者： Srinath Doss Jothi Paranthaman Suseendran Gopalakrishnan Akila Duraisamy Souvik Pal Balaganesh Duraisamy Chung Le Van Dac-Nhuong Le Faculty of Computing Botho UniversityGaboroneBotswana Department of Computer Science Gaborone University College of Law and Professional StudiesGaboroneBotswana Vels Institute of Science Technology&Advanced StudiesChennai600117India Global Institute of Management and Technology West Bengal741102India Faculty of Computer Science and Multimedia Lincoln University CollegeKelantan15050Malaysia Center for Visualization&Simulation Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical *** datum is sensitive,and hence security is a must in transforming the sensational *** this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text *** encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 *** reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded *** show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed *** results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable *** the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.

关键词： Internet of Things medical images cryptography memetic algorithm steganography

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First Competition on Presentation Attack Detection on ID Card

arXiv

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arXiv 2024年

作者： Tapia, Juan E. Damer, Naser Busch, Christoph Espin, Juan M. Barrachina, Javier Rocamora, Alvaro S. Ocvirk, Krištof Alessio, Leon Batagelj, Borut Patwardhan, Sushrut Ramachandra, Raghavendra Mudgalgundurao, Raghavendra Raja, Kiran Schulz, Daniel Aravena, Carlos da/sec-Biometrics and Internet Security Research Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Facephi company Spain Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Gjøvik Norway Santiago Chile

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available;therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be"Anonymous" reached the best average ranking results of 74.80%, followed very closely by the"IDVC" team with 77.65%. © 2024, CC BY-NC-ND.

关键词： Biometrics

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Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions

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computers in Biology and Medicine 2024年 171卷 108199-108199页

作者： Saad, Fatima Frysch, Robert Saalfeld, Sylvia Kellnberger, Stephan Schulz, Jessica Fahrig, Rebecca Bhadra, Krish Nürnberger, Andreas Rose, Georg Institute for Medical Engineering Otto-von-Guericke University Magdeburg Germany Forschungscampus STIMULATE Otto-von-Guericke University Magdeburg Germany Department of Simulation and Graphics Otto-von-Guericke University Magdeburg Germany Siemens Healthcare GmbH Forchheim Germany CHI Memorial Rees Skillern Cancer Institute Chattanooga United States Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting. © 2024

关键词： Image registration

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